Biometric image-based identification/verification systems (also referred to herein as matching systems) have played a critical role in modern society in both criminal and civil applications. For example, criminal identification in public safety sectors is an integral part of any present day investigation. Similarly in civil applications such as credit card or personal identity fraud, print identification, for instance, has become an essential part of the security process. Among all of the biometrics (face, fingerprint, iris, etc.), iris and retina are the preferred biometric indicators for high security applications. However, verification systems based on fingerprints are very popular both for historical reasons and for their proven performance in the field, and facial image matching is the second largest biometric indicator used for identification.
An automatic biometric image-based identification operation, e.g., for enabling fingerprint, palm print, or facial image identification, typically consists of two stages. The first is the registration or enrollment stage, and the second is the identification, authentication or verification stage. In the enrollment stage, an enrollee's personal information and biometric image (e.g., fingerprint, palm print, facial image, etc.) is enrolled in the system. The biometric image may be captured using an appropriate sensor, and features of the biometric image are generally extracted. In the case of fingerprints, such matching features may include, but are not limited to, a classification of each print as one of four major print types (i.e., arch, left loop, right loop and whorl) as well as minutiae that have respective X-Y coordinate positions and angles of orientation as is well known in the art. The personal information and extracted features, and perhaps the image, are then typically used to form a file record that is saved into a database for use in subsequent identification or verification of the enrollee.
In the identification/verification stage, a biometric image may be captured from an individual or a latent image may be obtained. Features are generally extracted from the image and, along with personal information, are formed into what is typically referred to as a search record. The search record is then compared with the enrolled (i.e., file) record(s) in the database of the identification system. One or more matched scores are typically generated as a result of this matching process, wherein each matched score is a measurement of similarity, for example between the matching features of the identified search and file records or images. Typically, the higher the matched score, the greater the similarity is determined to be. In one to one matching, for example, to determine whether a person is a previously enrolled person, the matched score between the search and file images is compared with a pre-determined threshold (also referred to herein as a verification threshold). If the matched score is greater than the pre-determined threshold, the person's identity is verified. Otherwise, the person cannot be verified as the person he or she claims to be.
The accuracy of a biometric verification system may be characterized by two types of measurements: a true accept rate (TAR) and a false accept rate (FAR). The TAR is a measurement, which describes how accurate a system is at accepting a legitimate fingerprint, for instance, from a person. The FAR is a measurement, which describes the level at which a system accepts a false claim from a person. Thus, the goal of designing a system is to achieve a very high TAR and a very low FAR. However, there is a trade-off between these two measurements, and one cannot simultaneously maximize the performance of both TAR and FAR.
For a given verification threshold, the TAR and FAR ratio of a matching system or apparatus is fixed. For a different verification threshold, the TAR and FAR numbers may be different. Normally the higher the verification threshold, the higher TAR and FAR will be (and vice versa) for the relationship between the verification threshold and the two measurements. A Receiver Operator Characteristic (ROC) curve may be used to characterize the trade-off between TAR and FAR. An ROC curve based upon an ROC analysis is an effective method of evaluating the performance of a given system. For an image-based identification/verification system as described above, the ROC curve is defined as a plot of TAR vs. FAR. The TAR is the percentage of genuine matched pairs whose matching score on the ROC curve is greater than or equal to the verification threshold and the FAR is the percentage of non-mated pairs whose matching score on the ROC curve is less than the verification threshold.
As discussed above, the verification threshold in authentication is used to evaluate whether two prints come from the same person. The threshold for prior art image-based identification/verification systems is typically determined and set based on the desired accuracy requirement for the system and based on a single ROC curve. This verification threshold then typically remains fixed for the system. However, a single fixed threshold found on the ROC curve in the prior art may not be optimal for an image-based authentication/verification system.
For example with respect to fingerprint matching systems, unfortunately the scores from two mated prints from the same person or from different people typically vary widely in the real world. The score differences are mainly caused by differences in the matching features, including mated minutiae, extracted from the two prints. For example, a different number of minutiae may be extracted from prints captured from the same person at different times for a variety of reasons including, but not limited to, the way of the person's prints were captured, smudges in the prints, over ink, under ink, using different capture sensors at different times, etc.
Accordingly, false minutiae may be detected and some true minutiae may not be detected in a print due to poor image quality prints. Moreover, differences in extracted minutiae may result from two prints having a different classification. For example, arch type fingerprints typically have fewer average true mated minutiae than that of whorl type fingerprints. Therefore, for reasons including those discussed above the matching score for two prints from the same person can be very different from time to time, finger to finger, pattern to pattern, and person to person.
Thus, there exists a need for a biometric identification system in which a dynamic threshold for the print verification process may be determined based at least on print quality and classification characteristics of a candidate print being evaluated.